Conflict-Aware Adaptive Cross-Reconstruction for Multimodal Sentiment Analysis

Disentanglement-based methods for learning shared representations are widely used in multimodal sentiment analysis. However, existing methods often overlook potential emotional conflicts across modalities within the same sample. They typically adopt intra-modal reconstruction and rely on similarity losses to align shared representations, which may distort the shared semantics under such conflicts. To address these, we propose a Conflict-aware Adaptive Cross-Reconstruction approach (CACR). First, we formally define emotional conflict and design a conflictaware weighting strategy. This strategy calculates conflict scores for each modality and maps them to the corresponding cross-reconstruction weights. Second, we construct a cross-reconstruction module. It reconstructs each target modality using its own specific features and the shared features of other modalities, achieving implicit alignment of shared representations. Coupled with the aforementioned weights, this module effectively suppresses conflicting modalities and mitigates semantic ambiguity. Furthermore, we develop a fine-grained sentiment refinement module that captures fine-grained cues from visual-and audiospecific features to supplement textual semantics. Extensive experiments on three datasets show that CACR outperforms existing state-of-the-art methods, demonstrating its effectiveness in handling emotional conflict.